Load sensor calibration method and apparatus, and computer-readable storage medium

Abstract

A load sensor calibration method and apparatus, and a computer-readable storage medium are provided. The load sensor calibration method includes: acquiring operation data of a wind turbine generator set, wherein the operation data comprises data of the wind turbine generator set operating in a normal operating state or data of the wind turbine generator set operating under different predetermined pitch angle idling states; generating a calibration training set on the basis of the operation data; and calibrating a load sensor according to the calibration training set.

1 . A method for calibrating a load sensor, comprising:
 obtaining operation data of a wind turbine, wherein the operation data comprises data of the wind turbine operating in a normal state or data of the wind turbine operating in an idle state under different predetermined pitch angles;   generating, based on the operation data, a training set for calibration; and   calibrating the load sensor based on the training set for calibration.   
     
     
         2 . The method according to  claim 1 , wherein:
 the operation data comprises data of the wind turbine operating in the normal state, and   the generating, based on the operation data, a training set for calibration comprises:   filtering the operation data based on a preset filtering condition to obtain a first dataset;   dividing, based on a pitch angle for each piece of the operation data in the first dataset, the first dataset into a set for pitch angles at idle and a set for pitch angles not at idle, wherein the set for pitch angles not at idle comprises one or more pieces of the operation data in the first dataset not having a pitch angle at idle; and   performing random sampling on operation data in the set for pitch angles at idle and operation data in the set for pitch angles not at idle, respectively, to obtain data points, and generating the training set for calibration based on azimuth angles in the data points, wherein each of the data points is a piece of the operation data at a time instant.   
     
     
         3 . The method according to  claim 2 , wherein the performing random sampling on operation data in the set for pitch angles at idle and operation data on operation data in the set for pitch angles not at idle, respectively, to obtain data points, and generating the training set for calibration based on azimuth angles in the data points comprises:
 obtaining the data points from the set for pitch angles at idle and the set for pitch angles not at idle through the random sampling;   distributing the data points into corresponding azimuth bins, based on the azimuth angles in the data points, until a quantity of the data points in each of the azimuth bins reaches a predetermined value; and   merging the data points from all the azimuth bins to generate the training set for calibration.   
     
     
         4 . The method according to  claim 3 , wherein
 each of the azimuth bins is a storage area corresponding to an angle interval of angle intervals determined by evenly dividing 360 degrees, wherein each azimuth bin covers a first predetermined angle,   each angle interval corresponds to two azimuth bins, and wherein the two azimuth bins correspond to the set for pitch angles at idle and the set for pitch angles not at idle, respectively.   
     
     
         5 . The method according to  claim 2 , wherein:
 the obtaining operation data of a wind turbine comprises   obtaining operation data of the wind turbine at equal intervals within a predetermined time period, as datasets; and   the filtering the operation data based on a preset filtering condition comprises   filtering, based on the preset filtering condition, each of the datasets obtained at the equal intervals to obtain the first dataset.   
     
     
         6 . The method according to  claim 1 , wherein
 the operation data comprises data of the wind turbine operating in the idle state under different predetermined pitch angles, and   the obtaining operation data of a wind turbine comprises:   obtaining first idle data, wherein the first idle data comprises operation data of the wind turbine idling under a first predetermined pitch angle for at least three cycles;   obtaining second idle data, wherein the second idle data comprises operation data of the wind turbine idling under a second predetermined pitch angle for at least three cycles; and   merging the first idle data and the second idle data to obtain a second dataset as the operation data.   
     
     
         7 . The method according to  claim 6 , wherein the generating, based on the operation data, a training set for calibration comprises:
 determining the second dataset as the training set for calibration.   
     
     
         8 . The method according to  claim 6 , wherein:
 the first predetermined pitch angle is greater than or equal to a second predetermined angle,   the second predetermined pitch angle is greater than or equal to the second predetermined angle, and   a difference between the first predetermined pitch angle and the second predetermined pitch angle is greater than a third predetermined angle.   
     
     
         9 . The method according to  claim 1 , wherein the obtaining operation data of a wind turbine comprises:
 obtaining the operation data of the wind turbine continuously within a predetermined time period; or   obtaining the operation data of the wind turbine at equal intervals within a predetermined time period.   
     
     
         10 . The method according to  claim 1 , wherein the calibrating the load sensor based on the training set for calibration comprises:
 obtaining, based on the training set for calibration, an actual strain of a load for the load sensor; and   calibrating the load sensor based on a theoretical strain, the actual strain and a minimized loss function, wherein the theoretical strain refers to a theoretical measurement of a strain due to the load.   
     
     
         11 . The method according to  claim 1 , wherein the method is applied to at least one of a standalone controller or a field-level controller. 
     
     
         12 . A device for calibrating a load sensor, comprising:
 a memory storing instructions, and   a processor, wherein   the processor, when executing the instructions, is configured to:
 obtain operation data of a wind turbine, wherein the operation data comprises data of the wind turbine operating in a normal state or data of the wind turbine operating in an idle state under different predetermined pitch angles; 
 generate, based on the operation data, a training set for calibration; and 
 calibrate the load sensor based on the training set for calibration. 
   
     
     
         13 . The device according to  claim 12 , wherein the processor is further configured to:
 in a case that the operation data comprises data of the wind turbine operating in the normal state,
 filter the operation data based on a preset filtering condition, to obtain a first dataset; 
 divide, based on a pitch angle for each piece of the operation data in the first dataset, the first dataset into a set for pitch angles at idle and a set for pitch angles not at idle, wherein the set for pitch angles not at idle comprises one or more pieces of the operation data in the first dataset not having a pitch angle at idle; and 
 perform random sampling on operation data in the set for pitch angles at idle and operation data on operation data in the set for pitch angles not at idle, respectively, to obtain data points, and generating the training set for calibration based on azimuth angles in the data points, wherein each of the data points is a piece of the operation data at a time instant. 
   
     
     
         14 . The device according to  claim 13 , wherein processor is further configured to:
 obtain the data points from the set for pitch angles at idle and the set for pitch angles not at idle through the random sampling;   distribute the data points into corresponding azimuth bins, based on the azimuth angles in the data points, until a quantity of the data points in each of the azimuth bins reaches a predetermined value; and   merge the data points from all the azimuth bins to generate the training set for calibration.   
     
     
         15 . The device according to  claim 14 , wherein:
 each of the azimuth bins is a storage area corresponding to an angle interval of angle intervals determined by evenly dividing 360 degrees, wherein each azimuth bin covers a first predetermined angle,   each angle interval corresponds to two azimuth bins, and wherein the two azimuth bins correspond to the set for pitch angles at idle and the set for pitch angles not at idle, respectively.   
     
     
         16 . The device according to  claim 13 , wherein the processor is further configured to:
 obtain operation data of the wind turbine at equal intervals within a predetermined time period, as datasets; and   filter, based on the preset filtering condition, each of the datasets obtained at the equal intervals to obtain the first dataset.   
     
     
         17 . The device according to  claim 12 , wherein the processor is further configured to:
 in a case that the operation data comprises data of the wind turbine operating in the idle state under different predetermined pitch angles,
 obtain first idle data, wherein the first idle data comprises operation data of the wind turbine idling under a first predetermined pitch angle for at least three cycles; 
 obtain second idle data, wherein the second idle data comprises operation data of the wind turbine idling under a second predetermined pitch angle for at least three cycles; and 
 merge the first idle data and the second idle data to obtain a second dataset as the operation data. 
   
     
     
         18 . The device according to  claim 17 , wherein the processor is further configured to determine the second dataset as the training set for calibration. 
     
     
         19 . (canceled) 
     
     
         20 . (canceled) 
     
     
         21 . The device according to  claim 12 , wherein the processor is further configured to:
 obtain, based on the training set for calibration, an actual strain of a load for the load sensor, and   calibrate the load sensor based on a theoretical strain, the actual strain and a minimized loss function, wherein the theoretical strain refers to a theoretical measurement of a strain due to the load.   
     
     
         22 . (canceled) 
     
     
         23 . A non-transitory computer-readable storage medium storing instructions, wherein
 the instructions, when executed by at least one computing device, cause the computing device to perform the method according to  claim 1 .   
     
     
         24 . (canceled)